School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.
Emory-Children's Cystic Fibrosis Center, Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, Georgia, USA.
mBio. 2023 Feb 28;14(1):e0306722. doi: 10.1128/mbio.03067-22. Epub 2022 Dec 8.
Our understanding of how bacterial pathogens colonize and persist during human infection has been hampered by the limited characterization of bacterial physiology during infection and a research bias toward , fast-growing bacteria. Recent research has begun to address these gaps in knowledge by directly quantifying bacterial mRNA levels during human infection, with the goal of assessing microbial community function at the infection site. However, mRNA levels are not always predictive of protein levels, which are the primary functional units of a cell. Here, we used carefully controlled chemostat experiments to examine the relationship between mRNA and protein levels across four growth rates in the bacterial pathogen Pseudomonas aeruginosa. We found a genome-wide positive correlation between mRNA and protein abundances across all growth rates, with genes required for P. aeruginosa viability having stronger correlations than nonessential genes. We developed a statistical method to identify genes whose mRNA abundances poorly predict protein abundances and calculated an RNA-to-protein (RTP) conversion factor to improve mRNA predictions of protein levels. The application of the RTP conversion factor to publicly available transcriptome data sets was highly robust, enabling the more accurate prediction of P. aeruginosa protein levels across strains and growth conditions. Finally, the RTP conversion factor was applied to P. aeruginosa human cystic fibrosis (CF) infection transcriptomes to provide greater insights into the functionality of this bacterium in the CF lung. This study addresses a critical problem in infection microbiology by providing a framework for enhancing the functional interpretation of bacterial human infection transcriptome data. Our understanding of bacterial physiology during human infection is limited by the difficulty in assessing bacterial function at the infection site. Recent studies have begun to address this question by quantifying bacterial mRNA levels in human-derived samples using transcriptomics. One challenge for these studies is the poor predictivity of mRNA for protein levels for some genes. Here, we addressed this challenge by measuring the transcriptomes and proteomes of P. aeruginosa grown at four growth rates. Our results revealed that the growth rate does not impact the genome-wide correlation of mRNA and protein levels. We used statistical methods to identify the genes for which mRNA and protein were poorly correlated and developed an RNA-to-protein (RTP) conversion factor that improved the predictivity of protein levels across strains and growth conditions. Our results provide new insights into mRNA-protein correlations and tools to enhance our understanding of bacterial physiology from transcriptome data.
我们对细菌病原体如何在人类感染过程中定植和持续存在的理解受到了限制，这是因为在感染过程中对细菌生理学的特征描述有限，以及研究偏向于快速生长的细菌。最近的研究开始通过直接量化人类感染过程中的细菌 mRNA 水平来解决这些知识空白，其目的是评估感染部位的微生物群落功能。然而，mRNA 水平并不总是可以预测蛋白质水平，而蛋白质水平才是细胞的主要功能单位。在这里，我们使用精心控制的恒化器实验，在细菌病原体铜绿假单胞菌的四个生长率下研究了 mRNA 和蛋白质水平之间的关系。我们发现，在所有生长率下，mRNA 和蛋白质丰度之间存在全基因组正相关，对于铜绿假单胞菌生存能力所必需的基因比非必需基因具有更强的相关性。我们开发了一种统计方法来识别那些 mRNA 丰度不能很好地预测蛋白质丰度的基因，并计算了 RNA 到蛋白质 (RTP) 的转换因子以改善 mRNA 对蛋白质水平的预测。该 RTP 转换因子在公开的转录组数据集上的应用具有高度稳健性，能够更准确地预测不同菌株和生长条件下的铜绿假单胞菌蛋白质水平。最后，该 RTP 转换因子被应用于铜绿假单胞菌人类囊性纤维化 (CF) 感染转录组，以提供对该细菌在 CF 肺中的功能的更深入了解。这项研究通过提供增强对细菌人类感染转录组数据的功能解释的框架，解决了感染微生物学中的一个关键问题。我们对人类感染期间细菌生理学的理解受到了在感染部位评估细菌功能的困难的限制。最近的研究开始通过使用转录组学在人类来源的样本中量化细菌 mRNA 水平来解决这个问题。这些研究面临的一个挑战是，对于某些基因，mRNA 对蛋白质水平的预测性很差。在这里，我们通过测量在四个生长率下生长的铜绿假单胞菌的转录组和蛋白质组来解决这个问题。我们的结果表明，生长率不会影响全基因组范围内 mRNA 和蛋白质水平的相关性。我们使用统计方法来识别那些 mRNA 和蛋白质相关性较差的基因，并开发了一种 RNA 到蛋白质 (RTP) 的转换因子，该因子提高了跨菌株和生长条件的蛋白质水平的预测性。我们的结果提供了关于 mRNA-蛋白质相关性的新见解，并提供了从转录组数据增强我们对细菌生理学理解的工具。
